Channel estimation method of RIS auxiliary millimeter wave system based on compressed sensing

Abstract

The invention relates to a channel estimation method of an RIS auxiliary millimeter wave system based on compressed sensing. The method comprises the following steps: step 1, establishing a communication system model based on a Saleh-Valenzuela channel model; step 2, acquiring a received signal matrix of the kth user; step 3, cascading the channel from the base station to the RIS and the channel from the RIS to the user into a cascade channel, and obtaining a cascade channel matrix; 4, after T moments, the kth user sends an orthogonal pilot signal to the base station, and an initial receiving signal matrix of the base station is obtained based on the cascade channel matrix; 5, converting the cascade channel matrix from a spatial domain to a virtual angular domain based on the sparsity of millimeter waves to obtain sparse representation of channel estimation; and 6, matrix recovery is carried out by adopting an improved compressed sensing algorithm based on row-structure sparseness, and channel estimation is completed. Compared with the prior art, the method has the advantages that the estimation performance and the estimation precision are improved, the overhead of pilot frequency numbers is reduced, and the like.

Description

technical field [0001] The present invention relates to the technical field of wireless communication physical layer, in particular to a channel estimation method of RIS-assisted millimeter wave system based on compressed sensing. Background technique [0002] Since a large number of reflection units greatly increase the dimension of the wireless channel, traditional channel estimation schemes require huge pilot overhead, and the hardware implementation of RIS is currently a nearly passive component, so it cannot actively perform signal processing and other related issues. The scheme using mathematical methods needs to perform multiple complex transformations on the channel matrix, which will bring extremely high computational complexity; the scheme using neural networks requires a lot of simulation and training for RIS scenarios, because RIS is still in the Conceived, the situation cannot be implemented at scale, so the large amount of training data will cause difficulties....

AI Technical Summary

Problems solved by technology

[0002] Because a large number of reflection units greatly increase the dimension of the wireless channel, the traditional channel estimation scheme requires a huge pilot overhead, and the hardware implementation of RIS is currently an approximately passive component, so it cannot actively perform signal processing and other related issues

The scheme using mathematical methods needs to perform multiple complex transformations on the channel matrix, which will bring extremely high computational complexity; the scheme using neural networks requires a lot of simulation and training for RIS scenarios, because RIS is still in the Conceived, not implemented at scale, so a large amount of training data creates difficulties

[0003] The current research on channel estimation of RIS-assisted communication systems, in addition to using traditional schemes, also uses neural networks, mathematical methods and knowledge related to compressed sensing. Existing technologies can improve the accuracy of channel estimation through high computational complexity and The effect of reducing pilot overhead, but a large increase in computational complexity will also cause problems such as high time cost, and using the greedy algorithm in the compressed sensing algorithm, the computational complexity is low, but even through multiple loop iterations, for the calculation Limited increase in complexity

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